Python Programming Recipes

For more than a decade, has hosted a vast repository of code recipes and discussions over at https://code./ – but in recent years it began to seem out-of-sync with the way developers shared code. New users and recipe updates had to be disabled to prevent spam and yet there remained a treasure-trove of interesting and useful pieces of code in this archive.

So, as the first phase in our ongoing effort to update and improve our community sites, we’ve migrated the entire library of code recipes (more than 5K recipes, 15K files and a mind-boggling >700K lines of code!) to GitHub: https://github.com//code. It’s become clear that GitHub is the central repository where developers share code, and to make it easier we’ve transferred all of the recipes, along with their descriptive text and original licensing terms to our new GitHub repo.

As a result, the recipes repo is now back open-for-business, and anyone with a GitHub account can submit changes, issues, or new recipes in any language for inclusion. We have a Wiki where you can read the instructions on how to submit recipes or revisions, get updates on new developments, or browse the list of every recipe broken down by language.

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With more than 4500 recipes, our Python code recipes collection is the largest anywhere, and there are recipes available in many other languages such as Perl, Ruby and C. We’d encourage you to submit recipes in all languages though, and would love to see a new collection of Go, Node.js, or Lua recipes start to take shape in there!

This is only the beginning – we’ve got lots of great improvements and expansions planned for both the code repo and our community sites in general, so follow us for updates! Stay tuned for search, recipes hosted in your own repos – and more!

Head over now to the new recipes home at: https://github.com//code to learn something new or submit a great code recipe of your own!

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© 2024 Software Inc. All rights reserved. ®, ActivePerl®, ActiveTcl®, ActivePython®, Komodo®, ActiveGo™, ActiveRuby™, ActiveNode™, ActiveLua™, and The Open Source Languages Company™ are all trademarks of .In case the title wasn’t clear, this blog post is about developing a web application using the Python programming language using Jupyter Lab, Flask, and the Heroku platform. If you were looking for an article on python recipes, you can start off with this one on making a poached Burmese python curry.

The problem with online baking recipes is that the majority of them use volumetric units. As any civilized baker would know, Patricia’s 1 cup of flour may very well be different than Patrick’s 1 cup of flour. Maybe Patricia sifted her flour. Maybe Patrick’s organic flour is a finer texture. Maybe both Pats should measure by mass instead of volume to avoid all this confusion in the first place.

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With this in mind, I was tired of constantly typing “1 tbsp baking soda in grams” and “3 1/4 cup flour in grams” into Google every time I came across a new recipe. There had to be a better way, and there was. As usual, Python was staring, patiently waiting for me to make eye contact with it.

My main reqiurement for this application was to be cross-platform. I wanted to be able to carry these conversions out on my laptop, someone else’s desktop, or my smartphone. Although React is what all the cool kids talk about these days, I also wanted to develop something fast. Since Python is both excellent at string parsing and easy to work with, it seemed like an appropriate choice.

After a bit of searching, I came across Flask and Heroku which would allow me to use Python for the entire development process, from prototype to deployment. Super cool.

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This blog post will outline the development of a simple string-parsing application that converts common ingredients measured in cups, tablespoons, and teaspoons to grams.

Sure you can do all your development in a text editor and command line. There’s nothing wrong with putting print statements everywhere, or even having a Python console open to do scratchpad-type testing. But when a tool as flexible and great as Jupyter is available for everyone to use, why settle for anything else?

The reason for Jupyter’s immense success is it excels in a form of programming called “literate programming”. It emphasizes a prose first approach where exposition with human-friendly text is punctuated with code blocks. It excels at demonstration, research, and teaching objectives - Unidata

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One of its greatest selling points is the option to execute code in chunks and see what the output is at each step. For data science, pairing this with the ability to write markdown above or below the code block makes documentation and explanations a joy to work with.

The proposed use case is described below. Nothing complex happening, just a handful of string parsing operations and a conversion table lookup.

*Turns out it’s easy to get clipboard data from a locally running script, but not as straightforward when the script is being hosted on a browser. I guess the user will just have to paste the ingredients into a textbox instead.

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Checking out my first working version, you’ll see that it’s not the most efficient script. However, when proving out a concept, the priority is to get something working (ie. a minimum viable product) and optimize later. Since uncertainty of this working (or being worth it) is quite high, we don’t want to spend too much time making it perfect!

Comparing that with the current version, we can see that the code structure went from messy and repetitive to organized and modular. Cleaning up code is always mildly therapeutic.

Now that we’ve finished testing the prototype and are happy with how it works, we can move on to the next step in turning it into a browser-based application.

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No, this Flask is different! There are many options for web development in Python, with two heavy hitters being Flask and Django. They’re both great frameworks, but where Django is the more fully-featured framework, Flask follows the Unix philosophy of “do one thing and do it well”.

Flask is a lightweight WSGI web application framework. It is designed to make getting started quick and easy, with the ability to scale up to complex applications. It began as a simple wrapper around Werkzeug and Jinja and has become one of the most popular Python web application frameworks. Flask offers suggestions, but doesn’t enforce any dependencies or project layout. It is up to the developer to choose the tools and libraries they want to use. There are many extensions provided by the community that make adding new functionality easy. - Flask GitHub

For myself (and this app), I chose Flask because of its simplicity and upfront/visible functionality. The function routing, template rendering is called explicitly with functions, with little magic happening “behind the scenes”. As this answer from StackOverflow puts it, “Django can be a little more mysterious for a beginner to figure out how everything fits together”.

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The simplest example of Flask is shown below (taken from Wikipedia). If you copy it below and execute the script, a blank webpage will render and display “Hello World!” in your browser.

Decorator. Modern web frameworks use routing techniques to help keep tracak of application URLs. This decorator is used to bind a Python function to a URL. In this case, the URL is

Looking at the code below, there isn’t much difference from the simple “hello world” application from above. One key change is the inclusion of

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GET - The browser tells the server to just get the information stored on that page and send it. POST - The browser tells the server that it wants to post some new information to that URL and that the server must ensure the data is stored and only stored once. This is how HTML forms usually transmit data to the server.

Since we are going to have a few buttons on our webpage, we need to include these HTTP methods. With multiple buttons, we can do a simple value check to handle them differently.

In the routed function call. Since we want to be able to format an HTML page, we can use a template (running on Jinja2) to create one.

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However, Gunicorn is only available on Unix systems. If we want to test our Heroku app locally, we’ll need to specify another command to run the app. For this, we’ll put the following in another file named

This is functionally equivalent to running the Flask command from before, with the main difference is that this uses Heroku’s local hosting framework.

, we need to add the dependencies our app has. When we deploy it to Heroku cloud, it will install these modules on the server. Add the following lines:

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To start a local server. However, since gunicorn is a webserver that isn’t available on Windows (but is required